Long‐term adaptive tracking via complementary trackers

In recent years, kernelised correlation filter‐based trackers have been employed to manage short‐term tracking problems and help long‐term trackers achieve excellent accuracy and robustness under challenging conditions, such as geometry/photometry changes, heavy occlusion, fast motion, motion blur, and out‐of‐camera view. Nonetheless, the inherent boundary effects and risky update strategy of correlation filters constrain the performance of short‐term tracking, which limits the performance of long‐term trackers. Moreover, the complicated redetection module leads to high‐computational cost, which results in the long‐term trackers to run at a low speed, thereby significantly restricting their applications. In the present work, the authors propose to employ complementary trackers in designing an efficient long‐term tracker. Furthermore, a sigmoid penalty coefficient is proposed to update the tracking model with an adaptive learning rate that adjusts the learning rate while the target encounters appearance variation. Finally, they propose a novel redetection method that combines a redetection classifier with a short‐term component to redetect the target while satisfying the explicit condition. The long‐term tracker proposed in this study is proven to perform real‐time speed of more than 65 frames per second and state‐of‐the‐art accuracy by the experimental result on several challenging benchmarks.